The document discusses the implications of Moore's Law on government technology and the evolution of smart disclosure, emphasizing the need for governments to adapt and innovate more rapidly. It highlights the importance of open data, real-time information, and algorithmic regulation in creating effective public platforms and addressing future challenges. The author advocates for a proactive regulatory approach that focuses on outcomes rather than mere data acquisition.

1. Some Context for Thinking About
The Future of Smart Disclosure
Tim O’Reilly
O’Reilly Media
Smart Disclosure Summit
March 30, 2012

2. “The skill of writing is to
create a context in which
other people can think.”
-Edwin Schlossberg

3. What technology trends tell us
about where smart disclosure
will ultimately take us

4. Moore’s Law First 10 Years
150.0
112.5
75.0 Gordon Moore
37.5
0
Jan 1, 1970 Jun 25, 1971 Dec 16, 1972 Jun 9, 1974 Dec 1, 1975 May 24, 1977 Nov 15, 1978 May 8, 1980
And the reason I’m looking to the future is because of Moore’s Law. As you recall, this law,
named after Intel co-founder Gordon Moore, predicts that computing power will double
every two years. As you can see that leads to accelerating increases in power.
In a recent talk at Code for America, Clay Johnson pointed out

5. Moore’s Law with Gov Drag
150.0
112.5
Clay Johnson
Society Gov
75.0
37.5
0
Jan 1, 1970 Jun 25, 1971 Dec 16, 1972 Jun 9, 1974 Dec 1, 1975 May 24, 1977 Nov 15, 1978 May 8, 1980
that the slow pace of government action, and slow procurement processes, put government
behind on the Moore’s Law curve.

6. Gov Vs. Moore 2011
300000000
225000000
150000000
75000000
0
Jan 1, 1970 Dec 16, 1972 Dec 1, 1975 Nov 15, 1978 Oct 30, 1981 Oct 14, 1984 Sep 29, 1987 Sep 13, 1990 Aug 28, 1993 Aug 12, 1996 Jul 28, 1999 Jul 12, 2002 Jun 26, 2005 Jun 10, 2008 May 26, 2
Over time, this compounds, putting government technology further and further behind the
private sector curve. As a result, it behooves government to try to shoot further ahead of the
target.
And that’s why I want to provide some context for thinking about the future.

7. Now, we’re all very excited about the potential to turn *this*

8. into *this* - new services like Billshrink, which helps people compare credit cards or wireless plans

9. And to make “choice engines” that work like Kayak

10. § kayak.com
or openTable.

11. The new healthcare.gov insurance finder is a good example of a government site that does this.

12. But I want to start somewhere more prosaic, with maps. Most of us remember when these things were on paper, right?
Interestingly, it was open government data that drove the transition to Geographic Information Systems, and ultimately
the electronic maps and directions we enjoy today.

13. Government data driving the mapping revolution
§ USGS and other survey maps
§ Street maps
§ Address databases
§ ...

14. GPS: A 21st century platform launched in 1973
§ Massive investment for uncertain
return
§ Policy decisions can have
enormous impact
§ Marketplaces take time to develop,
and go in unexpected directions
There are a lot of lessons from GPS.
Ronald Reagan the father of foursquare.

15. Lesson 1
Government is a platform
But the really big lesson I want to take from GPS is that government, at its best, is a platform. It does things that are hard, and
big,
and that enable the private sector. National highways, space travel, satellites, are good examples.
All the innovation that has come from the private sector in the location arena was only possible because government built the
platform.
I believe data is the platform for the 21st century.

16. MapQuest - the counterexample
Remember when online mapping services looked like this? This is mapquest, circa 2005, just before the arrival of Google Maps.

17. Lesson 2
It’s still really early.
Choice engines haven’t yet had their
“Google Maps moment”
There comes a time when someone cracks the code and things really start to hum along.

18. § Siri
That’s also something you see in Apple’s Siri, which bills itself as a “decision engine.” Humans give high level direction,
algorithms figure out the best answer, and try to take you there. It’s kind of a black box choice engine.

19. Google maps was not only much more interactive, it integrated many other sources of data, and turned itself into a data
and mapping platform for other services.

20. One of the most interesting additions to Google maps was transit data - again, something else that came from government

21. Many people don’t realize that transit data in google maps (and subsequently in other mapping services and smartphone apps)
actually began with an initiative from the city of Portland’s TriMet transit agency.

22. Lesson 3
Seek out commercial partners,
don’t just wait for them to come to you
Trimet didn’t just release their data, they actively reached out to Microsoft and Google to partner on their new data idea.
Google took them up on their proposal. Other services and other later cities joined in.

23. § Health datapalooza
This same kind of “developer outreach” characterized Todd Park’s work on open data at HHS. Rather than just opening
the data, he proactively sought out partners. The HHS open data initiative now features a thriving developer conference,
hundreds of apps, and several funded startups. I know that’s what you’re also trying to do with smart disclosure.

24. Lesson 4
Keep data formats simple
Another lesson from what Trimet and Google did with transit data was the development of a dirt simple data format that was
open and easy for other cities to copy, and for any application to read.

25. It’s simply a small collection of text files, listing the agency, the location of stops, the routes they fall on, and the scheduled
times
for each bus or train route at each stop.

26. But there’s another lesson in transit data. Back in 2009, there was a legal controversy in San Francisco when
NextBus Information Systems sued a small iphone developer for creating an app based on the real time transit data
collected by the NextBus GPS system in the SF Muni buses. Nextbus lost the claim; Muni had made sure the
contract allowed for open re-use. But be on the lookout for vendors trying to lock down data paid for with
public money.

27. Lesson 5
Open data policies matter, because private
parties will try to hoard data and claim it as their own

28. A device that knows
where I am better than I
do, a knowing assistant
telling me where to go
and how to get there.
Returning to the evolving saga of mapping data, let’s consider how mobile phones are transforming
mapping. A phone is ...

29. An Internet Operating System that Controls Access to Data
An application that depends on
cooperating cloud data services
operating in real time:
- Location
- Search
- Speech recognition
- Live Traffic
- Imagery
More than that, a smartphone depends on what you might call ... This has been a key framing metaphor for
my thinking for most of the past decade. I urge you to adopt that same frame, and understand how data
is becoming a new operating system, a new platform, and to think what is the appropriate role of government
as part of that platform

30. Lesson 6
Real-time data will become the norm.
Plan for that future!
This is the next lesson of the way mapping data is evolving on the mobile platform!

31. “Would you be willing to cross
the street with information that
was five minutes old?”
-Jeff Jonas
Jeff Jonas of IBM did a commercial a couple of years ago that asked this provocative question....
It’s becoming quite clear that real time data is going to be the norm.

32. And so, while I’m excited about smart disclosure applications like BillGuard, that warn me of suspicious transactions, I’ll
be even more excited as these systems drive smart alerts, and give me more control - for example, warning me when
I’m about to exceed my family budget with my next credit card purchase, and not just looking for fraud. Of course,
that assumes that cc companies would have your best interests at heart - which is why this kind of warning is more
likely to come from third party apps than from cc vendors.

33. Returning to maps...
It’s easy to take the blue dot for granted, but it’s a
wonder of real-time data coordination
Returning to maps, we see the role of real time in “the blue dot” that tells you where you are on your route.

34. § Combining data from multiple sources is critical
– GPS
– Cell tower triangulation
– WiFi signals
– But that’s only the beginning of the sensor revolution
In order to keep track of location, you really need to have access to multiple data sources. In cities, for instance, tall buildings
cut
off the view of GPS satellites. Cell tower triangulation and mapping of known wi-fi signals provides redundancy and greater
accuracy.

35. I was introduced just the other day to a new location platform called PlaceMe, which uses the sensors in the phone
to do even better real time location detection, mapping your location to venues and addresses without any effort
on your part.

36. § Uses the accelerometer to note when you’re walking,
running, driving, or stationary
§ Wakes up the location sensors every time you’re
stationary for a while
§ Logs the location and the length of time you were
there
§ Private, encrypted data store on your phone
§ A platform enabling private, high quality location and
movement data for location and “quantified self”
fitness apps
§ Completely automatic (except to correct locations if
wrong) and “always on”

37. It’s kind of eerie just how accurate it is.

38. Lesson 7
Getting privacy rules right is going to be
a matter of thoughtful tradeoffs
And of course, while this is a private app, not a social sharing app, the implications for privacy are enormous. We now carry
around
a sensor platform in our pocket, and it makes possible all kinds of new data services. And that leads me to Lesson 7...

39. The Google Autonomous Vehicle
And that leads me to the latest revolution in mapping - the Google Autonomous Vehicle.
I want to talk about this for several reasons. One of them is to remind you just how far the future of smart disclosure
might take us. This used to be a map! Then we had smarter interfaces to show humans how to get where they are
going. But ultimately, the data disappears into a device or service that just knows how to do the right thing.

40. 2005: Seven Miles in Seven Hours
But there’s another point I want to emphasize about the development of autonomous vehicles.
You see, back in 2005, when DARPA issued a Grand Challenge for autonomous vehicles, the winner went seven miles in seven
hours.

41. 2011: Hundreds of thousands of miles in ordinary traffic
Yet only six years later, Google has announced a vehicle that has gone ...

42. Artificial Intelligence
“the science and engineering
of making intelligent machines”
-John McCarthy, 1956
Was it a huge advance in AI, akin to what we saw when IBM’s Watson beat human champions at the game of Jeopardy?

43. But it isn’t just better AI
“We don’t have better algorithms. We just have more
data.” - Peter Norvig, Chief Scientist, Google
Peter Norvig says that the AI isn’t any better. Google just has more data. What kind of data?

44. It turns out that Google had human drivers drive all those streets in cars that were taking pictures, and taking very precise
measurements of distances to everything. The autonomous vehicle is actually remembering the route that was driven by human
drivers at some previous time. That “memory”, as recorded by the car’s electronic sensors, is stored in the cloud, and helps
guide the car. As Peter pointed out, “picking a traffic light out of the field of view of a video camera is a hard AI problem.
Figuring out if it’s red or green when you already know it’s there is trivial.”

45. Human-Computer Symbiosis
“The hope is that, in not too many years, human
brains and computing machines will be coupled
together very tightly, and that the resulting
partnership will think as no human brain has
ever thought and process data in a way not
approached by the information-handling
machines we know today.”
– Licklider, J.C.R., "Man-Computer Symbiosis", IRE
Transactions on Human Factors in Electronics, vol. HFE-1,
4-11, Mar 1960. Eprint
This is an example of what JCR Licklider, the DARPA program manager who originally funded the work on TCP/IP that brought
us the Internet, wrote about in his 1960 paper Man-Computer Symbiosis....

46. So when Google got “busted” for collecting wifi data, and policy makers didn’t understand why they might want to do that,
except for nefarious purposes, it was the policy makers who weren’t seeing far enough into the future.

47. Lesson 8:
Look at intent and outcomes,
not just acquisition of data
So here’s a piece of advice to policy makers:
We’re increasingly going to need a privacy regime that doesn’t focus on what data you collect or have, but on how you use it,
and regulates misuse, not possession.

48. It is precisely because of the overlap
between computers and human activity
that all this magic becomes possible
and it’s also important to note that the choice engines are increasingly algorithmic, operating as a kind of black box.

49. Lesson 9:
Beyond Smart Disclosure:
Feedback Loops and “Algorithmic Regulation”
I want to move on, and to talk a bit about where all this is taking us - towards systems that are algorthmically driven and
therefore must be “algorithmically regulated.” I’m told that “regulation” has become a dirty word in Washington, and that
we should just talk about making markets work better. Well, I’m not going to back down. One of the things that make markets
work better is the right kind of regulation. Your car’s carburetor or fuel injection system is a regulatory system. The autopilot
of an airplane is a regulatory system, and the Google self-driving car is a regulatory system, using algorithms (i.e. rules) and
feedback
loops to keep on course.

50. § credit card fraud detection as an ec
Credit card fraud detection is a great commercial example of algorithmic regulation. All kinds of data is mined and monitored
to detect abnormal patterns. Government regulation needs to move in this same direction. This requires a new sense of what
“regulation” means. It’s not the articulation of fixed rules of behavior, which are then monitored by periodic inspection, but
a set of rules (i.e. algorithms) that are constantly evolving in response to new data, new attacks, in order to achieve desired
outcomes.

51. § image of google search for “Smart Disclosure”
§ if good ads here, this could work for both search
quality and ad stuff
I’m not an expert on credit card fraud detection systems, so I’m going to explain the concept more thoroughly by looking at
another similar system, the algorithmic regulation by which Google ensures search quality, and by which it seeks out the
most relevant ads. A lot of people don’t realize how this works. Essentially google “tests” search quality by sending out a set of
sample
queries to thousands of testers with a simple question: are these good results? If the answer is “no,” they tweak the algorithm.
They
don’t fix individual problems.

52. “Half the money I spend
on advertising is wasted;
the trouble is I don't know
which half.”
- John Wanamaker
(1838-1922)
The first thing to understand is that algorithmic regulation depends on feedback loops that manage for outcomes.
Increasingly, technology is solving what we can call “the Wanamaker problem.”

53. When Google revolutionized the ad world by paying for clicks rather than page impressions, they were moving from a model
where you pay for some set of activities (we showed your ad 100,000 times) to one where you pay for outcomes (5000 people
clicked on it.) There’s a continuous measurement loop, and Google’s ability to outperform the competition depends on a
huge amount of data mining to predict what people are more likely to click on. Until recently, their competitors sold to the
highest
bidder, but Google realized that if you could predict likelihood of click, you could actually make more money ....

54. “Only 1% of healthcare spend now goes to diagnosis.
We need to shift from the idea that you do diagnosis
at the start, followed by treatment, to a cycle of
diagnosis, treatment, diagnosis...as we explore what
works.”
-Pascale Witz, GE Medical Diagnostics
We’re now seeing this same idea spread to other areas of the economy. For example, in healthcare, personalized medicine
requires new kinds of diagnostic feedback loops.

55. That’s also one focus of the Accountable Care Act - to pay for outcomes, not for procedures.

56. In the city of San Francisco, you’re seeing something similar, where all the parking meters are equipped with sensors, and
pricing varies by time of day, and ultimately by demand.

57. for profit colleges
AT first glance, the Education Department’s new regulations on for profit colleges’ eligibility for federal student loans
seems like a great attempt at algorithmic regulation until you look at the details. Only 35% of students
have to be able to repay their loans?

58. We really have to watch out for bad actors lobbying the system.

59. As a technologist, I was struck by the comparison with Google’s “Panda” search algorithm update, which penalized content
farms and other sites that were gaming the system to get higher search rankings. Imagine if Matt Cutts and Amit Singhal sat
down with the content farms and agreed to water down the update so that only 35% of the results were useful, to protect
the business model of the content farms?

60. Lesson 10:
Bad actors will always try to game the system.
So get some cojones and don’t be afraid of regulation.

61. This means regulatory independence. The Fed is probably the best example.

62. I want to return to billshrink. Is it really frequent flyer miles that make for the best credit card value? The real smart disclosure
we need here is which of these guys are charging the most in fees, which banks are clearing checks proactively in such a way
as to generate overdraft fees? So be very pointed in figuring out what data needs to be disclosed to really serve the consumer.

63. Lesson 11:
The secret of algorithmic data systems
is to focus on real time measurement of outcomes
And you need to think hard about what data will really support those outcomes. It may not be data you have now. You have
to be hungry for new data and new algorithms that give better results. Just like Google is. Just like hedge funds are. Just like
the private sector.

64. This shift requires new competencies of companies. The field has increasingly come to be called “Data Science” - extracting
meaning and services from data - and as you can see, the set of skills that make up this job description are in high demand
according to LinkedIn. They are literally going asymptotic.

65. “The legitimate object of government is to do for the
people what needs to be done, but which they cannot,
by individual effort, do at all, or do so well, for
themselves.”
-Abraham Lincoln
In closing, I want to return to the notion of government as a platform. When I first articulated that notion, I argued that
government is, at bottom, a mechanism for collective action, a means for doing things that are best done together. So
I was delighted recently to discover that Abraham Lincoln had said much the same thing 150 years ago. But this notion
also suggests a level of restraint. The best government programs enable the private sector; they don’t compete with it.
I hope that smart disclosure follows this lead, that it enables, and to use Richard Thaler’s notion, *nudges* the market
in the right direction to produce socially beneficial outcomes, but that it does so with a light hand. As the Chinese philosopher
Lao Tzu said three thousand years ago, “When the best leader leads, the people say ‘We did it ourselves.’”